Bibliographic record
Abstract
A native Gaelic speaker born in the Isle of Lewis and a graduate of Edinburgh University, Scotland, Catrìona NicÌomhair Parsons has been involved in the teaching of Gaelic language and song in North America for decades. For thirty summers, she taught Scottish Gaelic at the Gaelic College, St. Ann’s, Cape Breton, Nova Scotia, where she was commissioned to prepare Gàidhlig troimh Chòmhradh, a Gaelic course in three volumes with recorded text. For many years, she taught in the Celtic Studies Department of St. Francis Xavier University, Nova Scotia; after retiring, she spent six years working for the newly constituted Nova Scotia Office of Gaelic Affairs. She has written well over a hundred Gaelic-English articles for local newspapers. Her poetry has been published in Scottish Gaelic periodicals GAIRM and GATH, and she has produced her solo CD of Gaelic songs entitled “Eileanan mo Ghaoil” in tribute both to Cape Breton and Lewis. From Seattle, Washington, to Grandfather Mountain, North Carolina; from Toronto to Nova Scotia, Canada; from Sydney, Australia, to Dunedin, New Zealand, Catrìona has been privileged to share her beloved language and culture with motivated students, many of whom are now instructors themselves. \n \nThis, her most recent work, is a synthesis of all of the grammatical insights garnered from decades of experience teaching Scottish Gaelic to learners around the world. It clearly demonstrates in easy-to-read chapters, tables, and examples how the Gaelic language is structured. Rules, forms, pronunciation, and a host of other issues are all logically and systematically explained. Furthermore, this book can act as a handy reference for either the beginner or native speaker.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".